Suchergebnisse

Dairies within the United Kingdom are classified into two groups, namely, off‐farm and on‐farm dairies (the latter often being small scale). We propose a model for the probability of milk sold as pasteurized reaching the point of retail contaminated with Vero‐cytotoxigenic Escherichia coli (VTEC) O157 from each of these two pathways. We evaluate qualitatively the exposures inherent in each, and compare and contrast the two situations. The model framework is generic, in that it can in principle be used, with the relevant data modifications, to provide a qualitative assessment of the likely exposure from milk sold as pasteurized to any potentially milk‐borne pathogenic organism. Furthermore, the methodological approaches presented are widely applicable in the microbial risk assessment field. The specific example presented will be of particular interest to the UK dairy and public health communities: we conclude that the exposure potential per liter consumed from milk processed in off‐farm dairies is negligible, whereas the exposure potential per liter consumed from milk processed on‐farm is low, but not sufficiently small to be regarded as negligible. We also identify areas of data sparsity, which need to be addressed for quantitative risk assessment to proceed, and highlight the critical points in the pasteurized milk production chain, which, in the event of a breakdown, have the potential to increase the risk to the consumer.

OBJECTIVE To review the available evidence that examines the association between climatic and agricultural land use factors and the risks of enteric zoonoses in humans and consider information needs and possible pathways of intervention. METHODS The electronic databases PubMed, Web of Science and Embase and government websites were searched systematically for published literature that investigated the association of climatic and/or agricultural exposures with the incidence of the four most common enteric zoonotic diseases in New Zealand (campylobacteriosis, salmonellosis, cryptosporidiosis and giardiasis). Results The 16 studies in the review demonstrated significant associations between climate, agricultural land use and enteric disease occurrence. The evidence suggests that enteric disease risk from environmental reservoirs is pathogen specific. In some rural regions, environmental pathogen load is considerable, with multiple opportunities for zoonotic transmission. CONCLUSIONS Enteric disease occurrence in NZ is associated with climate variability and agricultural land use. However, these relationships interact with demographic factors to influence disease patterns. IMPLICATIONS Improved understanding of how environmental and social factors interact can inform effective public health interventions under scenarios of projected environmental change.

OBJECTIVE To review the available evidence that examines the association between climatic and agricultural land use factors and the risks of enteric zoonoses in humans and consider information needs and possible pathways of intervention. METHODS The electronic databases PubMed, Web of Science and Embase and government websites were searched systematically for published literature that investigated the association of climatic and/or agricultural exposures with the incidence of the four most common enteric zoonotic diseases in New Zealand (campylobacteriosis, salmonellosis, cryptosporidiosis and giardiasis). Results The 16 studies in the review demonstrated significant associations between climate, agricultural land use and enteric disease occurrence. The evidence suggests that enteric disease risk from environmental reservoirs is pathogen specific. In some rural regions, environmental pathogen load is considerable, with multiple opportunities for zoonotic transmission. CONCLUSIONS Enteric disease occurrence in NZ is associated with climate variability and agricultural land use. However, these relationships interact with demographic factors to influence disease patterns. IMPLICATIONS Improved understanding of how environmental and social factors interact can inform effective public health interventions under scenarios of projected environmental change.

A Bayesian approach was developed by Hald et al.(1) to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens. Our modified Hald model has several advantages over the original approach, which include the introduction of uncertainty in the estimates of source prevalence and an improved strategy for identifiability. We have applied our modified model to the two major food‐borne zoonoses in New Zealand, namely, campylobacteriosis and salmonellosis. Major challenges were the data quality for salmonellosis and the inclusion of environmental sources of campylobacteriosis. We conclude that by modifying the Hald model we have improved its identifiability, made it more applicable to countries with less intensive surveillance, and feasible for other pathogens, in particular with respect to the inclusion of nonfood sources. The wider application and better understanding of this approach is of particular importance due to the value of the model for decision making and risk management.

Abstract
The Asian community — the second largest non-European ethnic community in New Zealand — plays an important role in combatting the COVID-19 pandemic, evidenced by their active advocation for border control and mass masking. Despite the long history of racial discrimination against the Asian population, the Asian community has experienced certain degrees of racial discrimination associated with the stigmatisation as the cause of the COVID-19 outbreak in New Zealand. Based on data from a quantitative online survey with 402 valid responses within the Asian communities across New Zealand and the in-depth interviews with 19 Asian people in Auckland, New Zealand, this paper will illustrate Asian people's experience of racial discrimination and stigmatisation during the pandemic in the country. The survey shows that since the outbreak of COVID-19, under a quarter of the participants reported experiencing discrimination, and a third reported knowing an immediate contact who had experienced discrimination. However, when looking beyond their immediate social circle, an even higher proportion reported noticing racism and stigmatisation through the traditional or social media due to COVID-19. Major variations of the degree of racial discrimination experienced are determined by three demographic variables: ethnicity, age, and region. The in-depth interviews largely echoed the survey findings and highlighted a strong correlation between the perceived racial discrimination among the local Asian community and the stigmatisation associated with COVID-19. These findings are important for improving the way we manage future pandemics and other disasters within the context of the UN Sendai Framework for Disaster Risk Reduction.

AbstractEast Africa is a hotspot for foodborne diseases, including infection by nontyphoidal Salmonella (NTS), a zoonotic pathogen that may originate from livestock. Urbanization and increased demand for animal protein drive intensification of livestock production and food processing, creating risks and opportunities for food safety. We built a probabilistic mathematical model, informed by prior beliefs and dedicated stakeholder interviews and microbiological research, to describe sources and prevalence of NTS along the beef supply chain in Moshi, Tanzania. The supply chain was conceptualized using a bow tie model, with terminal livestock markets as pinch point, and a forked pathway postmarket to compare traditional and emerging supply chains. NTS was detected in 36 (7.7%) of 467 samples throughout the supply chain. After combining prior belief and observational data, marginal estimates of true NTS prevalence were 4% in feces of cattle entering the beef supply and 20% in raw meat at butcheries. Based on our model and sensitivity analyses, true NTS prevalence was not significantly different between supply chains. Environmental contamination, associated with butchers and vendors, was estimated to be the most likely source of NTS in meat for human consumption. The model provides a framework for assessing the origin and propagation of NTS along meat supply chains. It can be used to inform decision making when economic factors cause changes in beef production and consumption, such as where to target interventions to reduce risks to consumers. Through sensitivity and value of information analyses, the model also helps to prioritize investment in additional research.

AbstractCampylobacter jejuni and Campylobacter coli infections are the leading cause of foodborne gastroenteritis in high‐income countries. Campylobacter colonizes a variety of warm‐blooded hosts that are reservoirs for human campylobacteriosis. The proportions of Australian cases attributable to different animal reservoirs are unknown but can be estimated by comparing the frequency of different sequence types in cases and reservoirs. Campylobacter isolates were obtained from notified human cases and raw meat and offal from the major livestock in Australia between 2017 and 2019. Isolates were typed using multi‐locus sequence genotyping. We used Bayesian source attribution models including the asymmetric island model, the modified Hald model, and their generalizations. Some models included an "unsampled" source to estimate the proportion of cases attributable to wild, feral, or domestic animal reservoirs not sampled in our study. Model fits were compared using the Watanabe–Akaike information criterion. We included 612 food and 710 human case isolates. The best fitting models attributed >80% of Campylobacter cases to chickens, with a greater proportion of C. coli (>84%) than C. jejuni (>77%). The best fitting model that included an unsampled source attributed 14% (95% credible interval [CrI]: 0.3%–32%) to the unsampled source and only 2% to ruminants (95% CrI: 0.3%–12%) and 2% to pigs (95% CrI: 0.2%–11%) The best fitting model that did not include an unsampled source attributed 12% to ruminants (95% CrI: 1.3%–33%) and 6% to pigs (95% CrI: 1.1%–19%). Chickens were the leading source of human Campylobacter infections in Australia in 2017–2019 and should remain the focus of interventions to reduce burden.